| Date | Topic |
|---|---|
| 16.11.2023 | Data preparation and manipulation |
| 23.11.2023 | Basic statistics and data analysis with R |
| 23.11.2023 | Exercises/Workshop 4: Data gathering, data import |
| 30.11.2023 | Guest Lecture: Matteo Courthoud (Senior Economist and Data Scientist @Zalando) |
| Date | Topic |
|---|---|
| 07.12.2023 | Visualisation, dynamic documents |
| 07.12.2023 | Exercises/Workshop 5: Data preparation and applied data analysis with R |
| 14.12.2023 | Guest Lecture: Florian Chatagny (Head of Data Science @Federal Finance Administration in Bern) |
| 21.12.2023 | Exercises/Workshop 6: Visualization, dynamic documents |
| 21.12.2023 | Summary, Wrap-Up, Q&A, Feedback |
| 21.12.2023 | Exam for Exchange Students |
Be the JSON file
{
"students": [
{
"id": 19091,
"firstName": "Peter",
"lastName": "Mueller",
"grades": {
"micro": 5,
"macro": 4.5,
"data handling": 5.5
}
},
{
"id": 19092,
"firstName": "Anna",
"lastName": "Schmid",
"grades": {
"micro": 5.25,
"macro": 4,
"data handling": 5.75
}
},
{
"id": 19093,
"firstName": "Noah",
"lastName": "Trevor",
"grades": {
"micro": 4,
"macro": 4.5,
"data handling": 5
}
}
]
}
Write an R code to extract a table with, as a first column, a vector of first names, and as a second column, the average grade per student. The table can be a data frame or a tibble.
<students>
<student>
<id>19091</id>
<firstName>Peter</firstName>
<lastName>Mueller</lastName>
<grades>
<micro>5</micro>
<macro>4.5</macro>
<dataHandling>5.5</dataHandling>
</grades>
</student>
<student>
<id>19092</id>
<firstName>Anna</firstName>
<lastName>Schmid</lastName>
<grades>
<micro>5.25</micro>
<macro>4</macro>
<dataHandling>5.75</dataHandling>
</grades>
</student>
<student>
<id>19093</id>
<firstName>Noah</firstName>
<lastName>Trevor</lastName>
<grades>
<micro>4</micro>
<macro>4.5</macro>
<dataHandling>5</dataHandling>
</grades>
</student>
</students>
Noah Trevor are Anna Schmid and Peter Mueller<student id="19093" firstName="Noah" lastName="Trevor">
<grades micro="4" macro="4.5" dataHandling="5" />
</student>
Tell your future self what this script is all about
####################################################################### # Project XY: Data Gathering and Import # # This script is the first part of the data pipeline of project XY. # It imports data from ... # Input: links to data sources (data comes in ... format) # Output: cleaned data as CSV # # U. Matter, St. Gallen, 2018 ####################################################################### # SET UP -------------- # load packages library(tidyverse) # set fix variables INPUT_PATH <- "/rawdata" OUTPUT_FILE <- "/final_data/datafile.csv" # IMPORT RAW DATA FROM CSVs -------------
Following Wickham (2014):
Tidy data. Source: Wickham and Grolemund (2017), licensed under the Creative Commons Attribution-Share Alike 3.0 United States license.
Wickham, Hadley. 2014. “Tidy Data.” Journal of Statistical Software 59 (10): 1–23. https://doi.org/10.18637/jss.v059.i10.
Wickham, Hadley, and Garrett Grolemund. 2017. Sebastopol, CA: O’Reilly. http://r4ds.had.co.nz/.